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Proceedings Paper

Signal source separation and localization in the analysis of dynamic near-infrared optical tomographic time series
Author(s): Harry L. Graber; Yaling Pei; Randall Locke Barbour; David K. Johnston; Ying Zheng; John E. Mayhew
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Paper Abstract

The emerging sub-field of dynamic medical optical tomography shows great potential for conferring significantly enhanced early diagnosis and treatment monitoring capabilities upon researchers and clinicians. In previous reports we have showed that adoption of elementary time-series analysis techniques can bring about large large improvements in localization and contrast in optical tomographic images. Here we build upon the earlier work, and show that well-known techniques for extraction and localization of signals embedded in a noisy background, and for deconvolution of signal mixtures, also can be successfully applied to the problem of interpreting dynamic optical tomography data sets. A general linear model computation is used for the signal extraction/localization problem, while the deconvolution problem is addressed by means of a blind source separation technique extensively reported.

Paper Details

Date Published: 29 July 2003
PDF: 21 pages
Proc. SPIE 4955, Optical Tomography and Spectroscopy of Tissue V, (29 July 2003); doi: 10.1117/12.479467
Show Author Affiliations
Harry L. Graber, SUNY/Downstate Medical Ctr. (United States)
Yaling Pei, NIRx Medical Technologies LLC (United States)
Randall Locke Barbour, SUNY/Downstate Medical Ctr. (United States)
NIRx Medical Technologies LLC (United States)
David K. Johnston, Univ. of Sheffield (United Kingdom)
Ying Zheng, Univ. of Sheffield (United Kingdom)
John E. Mayhew, Univ. of Sheffield (United Kingdom)

Published in SPIE Proceedings Vol. 4955:
Optical Tomography and Spectroscopy of Tissue V
Britton Chance; Robert R. Alfano; Bruce J. Tromberg; Mamoru Tamura; Eva M. Sevick-Muraca, Editor(s)

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